Distributed Output-Feedback Consensus Control of Multiagent Systems with Unknown Output Measurement Sensitivity
Kuo Li, Changchun Hua, Xiu You, Xinping Guan
Abstract
This article addresses the leader-following consensus problem for multiagent systems with unknown output measurement sensitivity based on a fixed directed topology. Different from the existing works on the consensus problem, the output measurement sensitivity is described as a total unknown parameter. Under this nonideal condition, we put forward a distributed output-feedback consensus control algorithm for the first time. To start with, we design the novel distributed high-gain observer and a high-gain K-filter for each follower, which are utilized to reconstruct the state of the leader and the follower, respectively. Subsequently, the consensus problem is transformed into the stability problem by introducing appropriate state transformation. By using the backstepping control method, the distributed output-feedback controller with an adaptive law is designed for each follower with the help of a Nussbaum-type function. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus with the designed controller. Finally, the numerical simulation is presented to illustrate the effectiveness of theoretical results.